def splitData(dataFile, test_size):
# 加载数据集
header = ['user_id', 'item_id', 'rating', 'timestamp']
df = pd.read_csv(dataFile, sep='\t', names=header)
n_users = df.user_id.unique().shape[0]
n_items = df.item_id.unique().shape[0]
print('Number of users = ' + str(n_users) + ' | Number of movies = ' +
str(n_items))
train_data, test_data = cv.train_test_split(df, test_size=test_size)
print("数据量：", len(train_data), len(test_data))
return df, n_users, n_items, train_data, test_data
X_encoded[:, i] = X[:, i]
else:
label_encoder.append(preprocessing.LabelEncoder())
X_encoded[:, i] = label_encoder[-1].fit_transform(X[:, i])
X = X_encoded[:, :-1].astype(int)
y = X_encoded[:, -1].astype(int)
# Create SVM classifier
classifier = OneVsOneClassifier(LinearSVC(random_state=0))
# Train the classifier
classifier.fit(X, y)
# Cross validation
X_train, X_test, y_train, y_test = train_test_split.train_test_split(X, y, test_size=0.2, random_state=5)
classifier = OneVsOneClassifier(LinearSVC(random_state=0))
classifier.fit(X_train, y_train)
y_test_pred = classifier.predict(X_test)
# Compute the F1 score of the SVM classifier
f1 = train_test_split.cross_val_score(classifier, X, y, scoring='f1_weighted', cv=3)
print("F1 score: " + str(round(100*f1.mean(), 2)) + "%")
# Predict output for a test datapoint
input_data = ['37', 'Private', '215646', 'HS-grad', '9', 'Never-married', 'Handlers-cleaners', 'Not-in-family', 'White', 'Male', '0', '0', '40', 'United-States']
# Encode test datapoint
input_data_encoded = [-1] * len(input_data)
count = 0
for i, item in enumerate(input_data):